Optimization of Multiuser MIMO Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper investigates the multiuser multiple-input-multiple-output (MIMO) linear cooperative spectrum sensing optimization problem, in which the primary user (PU) and the cognitive radio (CR) are equipped with multiple antennas. By optimizing the different weights assigned on the received signals of CRs, the cooperative spectrum sensing optimization aims at maximizing the probability of detection given a targeted probability of false alarm. Statistical characteristics of parameters in MIMO cooperative spectrum sensing systems have been determined for the PU with a single antenna and the CR with multiple antennas, the PU with multiple antennas and the CR with a single antenna, and both the PU and the CR with multiple antennas. Because of the non-convex characteristic of the optimization problem, an alternative approach based on a genetic algorithm (GA) instead of convex approaches is proposed to find the optimal weight vectors without solution domain restrictions and convexity constraints. Furthermore, several classical GA crossover operators have been provided to investigate their effect on sensing performance. The simulation results show that, the reliability of spectrum sensing in cooperative spectrum sensing system can be significantly improved with multiple antennas. Furthermore, the GA method is a promising approach in addressing the cooperative spectrum sensing problem.